Investigating Accuracies of Classifications for Randomized Imbalanced Class Distributions
Fundamenta Informaticae - Cognitive Informatics, Cognitive Computing, and Their Denotational Mathematical Foundations (II)
Investigating Accuracies of Classifications for Randomized Imbalanced Class Distributions
Fundamenta Informaticae - Cognitive Informatics, Cognitive Computing, and Their Denotational Mathematical Foundations (II)
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In this paper, we present an evaluation of learning costs of rule evaluation models based on objective indices for an iterative rule evaluation support method in data min- ing post-processing. Post-processing of mined results is one of the key processes in a data mining process. However, it is difficult for human experts to find out valuable knowl- edge from several thousands of rules obtained with a large dataset with noises. To reduce the costs in such rule eval- uation task, we have developed the rule evaluation support method with rule evaluation models, which learn from ob- jective indices for mined classification rules and evalua- tions by a human expert for each rule. To estimate learn- ing costs for predicting human interests with objective rule evaluation indices, we have done the two case studies with actual data mining results, which include different phases of human interests. With regarding to these results, we discuss about the relationship between performances of learning al- gorithms and human hypothesis construction process.